Color image quantization with peak-picking and color space

Abstract

Color image quantization is a significant procedure of reducing the huge range of color values of a digital color image into a limited range. In this paper, an automated clustering of pixels and color quantization algorithm is proposed. The ideal number of representative colors is unknown beforehand in most color quantization algorithms. This is an important handicap in most practical cases. The proposed color quantization approach (PPCS) is able to automatically estimate an appropriate number of colors in a quantized palette. Hence, PPCS requires no number of representative colors to be set in advance. This algorithm has two main steps to follow: color palette design and pixel mapping. The color palette is generated by the combination of the entire peaks of all color component histograms. Such that, all color component histogram was smoothed in order to remove unreliable peaks. Next, unreliable colors will be removed from the palette. Then, each pixel in the image will be assigned to the cluster (unit color in the palette) which has the least Euclidean distance. To evaluate the ability of the PPCS, 22 images from Berkeley segmentation dataset have been randomly selected and tested with PPCS and also by two well-known quantization algorithms. The numerical evaluations have been carried out by using computation time, PSNR, MSE, and SSIM performance criteria. Both visual and numerical evaluations reveal that the proposed method presents promising quantization results. Such that, PPCS is ranked first, second, first and first according to PSNR, MSE, SSIM and computation time, respectively.